RiRPSSP: A unified deep learning method for prediction of regular and irregular protein secondary structures

Protein secondary structure prediction (PSSP) is an important and challenging task in protein bioinformatics. Protein secondary structures (SSs) are categorized in regular and irregular structure classes. Regular SSs, representing nearly 50% of amino acids consist of helices and sheets, whereas the remaining amino acids represent irregular SSs. beta-turns and gamma-turns are the most abundant irregular SSs present in proteins. Existing methods are well developed for separate prediction of regular and irregular SSs. However, for more comprehensive PSSP, it is essential to develop a uniform model to predict all types of SSs simultaneously. In this work, using a novel dataset comprising dictionary of secondary structure of protein (DSSP)-based SSs and PROMOTIF-based beta-turns and gamma-turns, we propose a unified deep learning model consisting of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) for simultaneous prediction of regular and irregular SSs. To the best of our knowledge, this is the first study in PSSP covering both regular and irregular structures. The protein sequences in our constructed datasets, RiR6069 and RiR513, have been borrowed from benchmark CB6133 and CB513 datasets, respectively. The results are indicative of increased PSSP accuracy.